Louisa Pragst

Cultural communication idiosyncrasies in human-computer interaction

Comunicacio presentada a: 17th Annual Meeting of the Special Interest Group on Discourse and Dialogue; celebrada del 13 al 15 de setembre de 2016 a Los Angeles, USA

Exploring the Applicability of Elaborateness and Indirectness in Dialogue Management

In this paper, we investigate the applicability of soft changes to system behaviour, namely changing the amount of elaborateness and indirectness displayed. To this end, we examine the impact of elaborateness and indirectness on the perception of human-computer communication in a user study. Here, we show that elaborateness and indirectness influence the user’s impression of a dialogue and discuss the implications of our results for adaptive dialogue management. We conclude that elaborateness and indirectness offer valuable possibilities for adaptation and should be incorporated in adaptive dialogue management.

KRISTINA: A Knowledge-Based Virtual Conversation Agent

We present an intelligent embodied conversation agent with linguistic, social and emotional competence. Unlike the vast majority of the state-of-the-art conversation agents, the proposed agent is constructed around an ontology-based knowledge model that allows for flexible reasoning-driven dialogue planning, instead of using predefined dialogue scripts. It is further complemented by multimodal communication analysis and generation modules and a search engine for the retrieval of multimedia background content from the web needed for conducting a conversation on a given topic. The evaluation of the 1st prototype of the agent shows a high degree of acceptance of the agent by the users with respect to its trustworthiness, naturalness, etc. The individual technologies are being further improved in the 2nd prototype.

Towards an Ontology-Driven Adaptive Dialogue Framework

In this paper, we describe the principles and technologies that underpin the development of an adaptive dialogue manager framework, tailored to carrying out human-agent conversations in a natural, robust and flexible manner. Our research focus is twofold. First, the investigation of dialogue strategies that can handle dynamically created user and system actions, while still enabling the agent to adapt its actions to various and possibly changing contexts. Second, the utilisation of rich semantic annotations for capturing background knowledge, as well as conversation topics and semantics of user utterances extracted through language analysis. The resulting annotations comprise the situational descriptions upon which reasoning takes place to recognise the conversation context and compile appropriate responses.

Recurrent neural network interaction quality estimation

Getting a good estimation of the Interaction Quality (IQ) of a spoken dialogue helps to increase the user satisfaction as the dialogue strategy may be adapted accordingly. Therefore, some research has already been conducted in order to automatically estimate the Interaction Quality. This article adds to this by describing how Recurrent Neural Networks may be used to estimate the Interaction Quality for each dialogue turn and by evaluating their performance on this task. Here, we will show that RNNs may outperform non-recurrent neural networks.

Towards a Multimedia Knowledge-Based Agent with Social Competence and Human Interaction Capabilities

We present work in progress on an intelligent embodied conversation agent in the basic care and healthcare domain. In contrast to most of the existing agents, the presented agent is aimed to have linguistic cultural, social and emotional competence needed to interact with elderly and migrants. It is composed of an ontology-based and reasoning-driven dialogue manager, multimodal communication analysis and generation modules and a search engine for the retrieval of multimedia background content from the web needed for conducting a conversation on a given topic.

A Multimodal Annotation Schema for Non-Verbal Affective Analysis in the Health-Care Domain

The development of conversational agents with human interaction capabilities requires advanced affective state recognition integrating non-verbal cues from the different modalities constituting what in human communication we perceive as an overall affective state. Each of the modalities is often handled by a different subsystem that conveys only a partial interpretation of the whole and, as such, is evaluated only in terms of its partial view. To tackle this shortcoming, we investigate the generation of a unified multimodal annotation schema of non-verbal cues from the perspective of an inter-disciplinary group of experts. We aim at obtaining a common ground-truth with a unique representation using the Valence and Arousal space and a discrete non-linear scale of values. The proposed annotation schema is demonstrated on a corpus in the health-care domain but is scalable to other purposes. Preliminary results on inter-rater variability show a positive correlation of consensus level with high (absolute) values of Valence and Arousal as well as with the number of annotators labeling a given video sequence.

Automatic Modification of Communication Style in Dialogue Management.

Comunicacio presentada a: INLG 2016 Workshop on Computational Creativity and Natural Language Generation, celebrat a Edinburgh, Escocia, del 5 al 8 de setembre de 2016.

EVA: A Multimodal Argumentative Dialogue System

This work introduces EVA, a multimodal argumentative Dialogue System that is capable of discussing controversial topics with the user. The interaction is structured as an argument game in which the user and the system select respective moves in order to convince their opponent. EVAs response is presented as a natural language utterance by a virtual agent that supports the respective content using characteristic gestures and mimic.

Challenges for adaptive dialogue management in the KRISTINA project

Access to health care related information can be vital and should be easily accessible. However, immigrants often have difficulties to obtain the relevant information due to language barriers and cultural differences. In the KRISTINA project, we address those difficulties by creating a socially competent multimodal dialogue system that can assist immigrants in getting information about health care related questions. Dialogue management, as core component responsible for the system behaviour, has a significant impact on the successful reception of such a system. Hence, this work presents the specific challenges of the KRISTINA project to adaptive dialogue management, namely the handling of a large dialogue domain and the cultural adaptability required by the envisioned dialogue system, and our approach to handling them.